@InProceedings{corona-thomason-mooney:2017:I17-2,
  author    = {Corona, Rodolfo  and  Thomason, Jesse  and  Mooney, Raymond},
  title     = {Improving Black-box Speech Recognition using Semantic Parsing},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {122--127},
  abstract  = {Speech is a natural channel for human-computer interaction in robotics and
	consumer applications.
	Natural language understanding pipelines that start with speech can have
	trouble recovering from speech recognition errors.
	Black-box automatic speech recognition (ASR) systems, built for general purpose
	use, are unable to take advantage of in-domain language models that could
	otherwise ameliorate these errors.
	In this work, we present a method for re-ranking black-box ASR hypotheses using
	an in-domain language model and semantic parser trained for a particular task.
	Our re-ranking method significantly improves both transcription accuracy and
	semantic understanding over a state-of-the-art ASR's vanilla output.},
  url       = {http://www.aclweb.org/anthology/I17-2021}
}